Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations8359
Missing cells24088
Missing cells (%)18.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory128.0 B

Variable types

Text4
Categorical3
Numeric9

Alerts

Critic_Count is highly overall correlated with User_CountHigh correlation
Critic_Score is highly overall correlated with User_CountHigh correlation
EU_Sales is highly overall correlated with Global_Sales and 2 other fieldsHigh correlation
Global_Sales is highly overall correlated with EU_Sales and 2 other fieldsHigh correlation
NA_Sales is highly overall correlated with EU_Sales and 2 other fieldsHigh correlation
Other_Sales is highly overall correlated with EU_Sales and 2 other fieldsHigh correlation
Platform is highly overall correlated with Year_of_ReleaseHigh correlation
User_Count is highly overall correlated with Critic_Count and 1 other fieldsHigh correlation
Year_of_Release is highly overall correlated with PlatformHigh correlation
Year_of_Release has 84 (1.0%) missing valuesMissing
Critic_Score has 4383 (52.4%) missing valuesMissing
Critic_Count has 4383 (52.4%) missing valuesMissing
User_Score has 3528 (42.2%) missing valuesMissing
User_Count has 4660 (55.7%) missing valuesMissing
Developer has 3489 (41.7%) missing valuesMissing
Rating has 3561 (42.6%) missing valuesMissing
Other_Sales is highly skewed (γ1 = 24.74795541)Skewed
NA_Sales has 2311 (27.6%) zerosZeros
EU_Sales has 3002 (35.9%) zerosZeros
JP_Sales has 4807 (57.5%) zerosZeros
Other_Sales has 3218 (38.5%) zerosZeros

Reproduction

Analysis started2025-12-03 10:32:13.348473
Analysis finished2025-12-03 10:32:23.819904
Duration10.47 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Name
Text

Distinct6231
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Memory size65.4 KiB
2025-12-03T10:32:24.304196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length132
Median length89
Mean length23.982653
Min length2

Characters and Unicode

Total characters200471
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5015 ?
Unique (%)60.0%

Sample

1st rowLEGO Batman: The Videogame
2nd rowLEGO Indiana Jones: The Original Adventures
3rd rowLEGO Batman: The Videogame
4th rowCombat
5th rowLEGO Harry Potter: Years 5-7
ValueCountFrequency (%)
the1505
 
4.5%
of867
 
2.6%
2587
 
1.8%
419
 
1.3%
no402
 
1.2%
3294
 
0.9%
super193
 
0.6%
world172
 
0.5%
star164
 
0.5%
ds146
 
0.4%
Other values (6216)28494
85.7%
2025-12-03T10:32:24.822363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24894
 
12.4%
e16060
 
8.0%
a13668
 
6.8%
o12411
 
6.2%
i10845
 
5.4%
r10610
 
5.3%
n10399
 
5.2%
t8548
 
4.3%
s7941
 
4.0%
l6189
 
3.1%
Other values (86)78906
39.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)200471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
24894
 
12.4%
e16060
 
8.0%
a13668
 
6.8%
o12411
 
6.2%
i10845
 
5.4%
r10610
 
5.3%
n10399
 
5.2%
t8548
 
4.3%
s7941
 
4.0%
l6189
 
3.1%
Other values (86)78906
39.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)200471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
24894
 
12.4%
e16060
 
8.0%
a13668
 
6.8%
o12411
 
6.2%
i10845
 
5.4%
r10610
 
5.3%
n10399
 
5.2%
t8548
 
4.3%
s7941
 
4.0%
l6189
 
3.1%
Other values (86)78906
39.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)200471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
24894
 
12.4%
e16060
 
8.0%
a13668
 
6.8%
o12411
 
6.2%
i10845
 
5.4%
r10610
 
5.3%
n10399
 
5.2%
t8548
 
4.3%
s7941
 
4.0%
l6189
 
3.1%
Other values (86)78906
39.4%

Platform
Categorical

High correlation 

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size65.4 KiB
DS
1106 
PS2
1104 
Wii
645 
PS3
643 
PSP
642 
Other values (26)
4219 

Length

Max length4
Median length3
Mean length2.7865773
Min length2

Characters and Unicode

Total characters23293
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowWii
2nd rowWii
3rd rowPSP
4th row2600
5th rowWii

Common Values

ValueCountFrequency (%)
DS1106
13.2%
PS21104
13.2%
Wii645
 
7.7%
PS3643
 
7.7%
PSP642
 
7.7%
X360588
 
7.0%
PS512
 
6.1%
GBA445
 
5.3%
PC439
 
5.3%
XB371
 
4.4%
Other values (21)1864
22.3%

Length

2025-12-03T10:32:24.963060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ds1106
13.2%
ps21104
13.2%
wii645
 
7.7%
ps3643
 
7.7%
psp642
 
7.7%
x360588
 
7.0%
ps512
 
6.1%
gba445
 
5.3%
pc439
 
5.3%
xb371
 
4.4%
Other values (21)1864
22.3%

Most occurring characters

ValueCountFrequency (%)
S5186
22.3%
P4429
19.0%
31502
 
6.4%
i1464
 
6.3%
D1424
 
6.1%
21192
 
5.1%
X1081
 
4.6%
B894
 
3.8%
6802
 
3.4%
G797
 
3.4%
Other values (15)4522
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)23293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S5186
22.3%
P4429
19.0%
31502
 
6.4%
i1464
 
6.3%
D1424
 
6.1%
21192
 
5.1%
X1081
 
4.6%
B894
 
3.8%
6802
 
3.4%
G797
 
3.4%
Other values (15)4522
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)23293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S5186
22.3%
P4429
19.0%
31502
 
6.4%
i1464
 
6.3%
D1424
 
6.1%
21192
 
5.1%
X1081
 
4.6%
B894
 
3.8%
6802
 
3.4%
G797
 
3.4%
Other values (15)4522
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)23293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S5186
22.3%
P4429
19.0%
31502
 
6.4%
i1464
 
6.3%
D1424
 
6.1%
21192
 
5.1%
X1081
 
4.6%
B894
 
3.8%
6802
 
3.4%
G797
 
3.4%
Other values (15)4522
19.4%

Year_of_Release
Real number (ℝ)

High correlation  Missing 

Distinct38
Distinct (%)0.5%
Missing84
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2006.3937
Minimum1980
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2025-12-03T10:32:25.071903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1995
Q12003
median2007
Q32010
95-th percentile2015
Maximum2017
Range37
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.0996209
Coefficient of variation (CV)0.0030400917
Kurtosis2.0666419
Mean2006.3937
Median Absolute Deviation (MAD)4
Skewness-1.1008033
Sum16602908
Variance37.205375
MonotonicityIncreasing
2025-12-03T10:32:25.213131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2008735
 
8.8%
2009696
 
8.3%
2010645
 
7.7%
2007625
 
7.5%
2011555
 
6.6%
2006510
 
6.1%
2005471
 
5.6%
2003419
 
5.0%
2004404
 
4.8%
2002375
 
4.5%
Other values (28)2840
34.0%
ValueCountFrequency (%)
19804
 
< 0.1%
198134
0.4%
198223
0.3%
198314
0.2%
19849
 
0.1%
19859
 
0.1%
198611
 
0.1%
198711
 
0.1%
198811
 
0.1%
198914
0.2%
ValueCountFrequency (%)
20173
 
< 0.1%
2016260
 
3.1%
2015304
3.6%
2014284
 
3.4%
2013279
 
3.3%
2012316
3.8%
2011555
6.6%
2010645
7.7%
2009696
8.3%
2008735
8.8%

Genre
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size65.4 KiB
Action
1743 
Role-Playing
912 
Misc
905 
Sports
879 
Adventure
741 
Other values (7)
3179 

Length

Max length12
Median length10
Mean length7.2732384
Min length4

Characters and Unicode

Total characters60797
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAction
2nd rowAction
3rd rowAction
4th rowAction
5th rowAction

Common Values

ValueCountFrequency (%)
Action1743
20.9%
Role-Playing912
10.9%
Misc905
10.8%
Sports879
10.5%
Adventure741
8.9%
Shooter584
 
7.0%
Platform565
 
6.8%
Racing525
 
6.3%
Fighting440
 
5.3%
Strategy390
 
4.7%
Other values (2)675
 
8.1%

Length

2025-12-03T10:32:25.334292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
action1743
20.9%
role-playing912
10.9%
misc905
10.8%
sports879
10.5%
adventure741
8.9%
shooter584
 
7.0%
platform565
 
6.8%
racing525
 
6.3%
fighting440
 
5.3%
strategy390
 
4.7%
Other values (2)675
 
8.1%

Most occurring characters

ValueCountFrequency (%)
t6078
 
10.0%
i5657
 
9.3%
o5613
 
9.2%
n4707
 
7.7%
e3697
 
6.1%
c3173
 
5.2%
r3159
 
5.2%
l3064
 
5.0%
a2738
 
4.5%
g2707
 
4.5%
Other values (17)20204
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)60797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t6078
 
10.0%
i5657
 
9.3%
o5613
 
9.2%
n4707
 
7.7%
e3697
 
6.1%
c3173
 
5.2%
r3159
 
5.2%
l3064
 
5.0%
a2738
 
4.5%
g2707
 
4.5%
Other values (17)20204
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t6078
 
10.0%
i5657
 
9.3%
o5613
 
9.2%
n4707
 
7.7%
e3697
 
6.1%
c3173
 
5.2%
r3159
 
5.2%
l3064
 
5.0%
a2738
 
4.5%
g2707
 
4.5%
Other values (17)20204
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t6078
 
10.0%
i5657
 
9.3%
o5613
 
9.2%
n4707
 
7.7%
e3697
 
6.1%
c3173
 
5.2%
r3159
 
5.2%
l3064
 
5.0%
a2738
 
4.5%
g2707
 
4.5%
Other values (17)20204
33.2%
Distinct295
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size65.4 KiB
2025-12-03T10:32:25.544239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length24
Mean length12.832635
Min length3

Characters and Unicode

Total characters107268
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)1.1%

Sample

1st rowWarner Bros. Interactive Entertainment
2nd rowLucasArts
3rd rowWarner Bros. Interactive Entertainment
4th rowAtari
5th rowWarner Bros. Interactive Entertainment
ValueCountFrequency (%)
entertainment1299
 
8.9%
interactive1130
 
7.7%
thq715
 
4.9%
nintendo706
 
4.8%
sony706
 
4.8%
computer705
 
4.8%
sega638
 
4.4%
games524
 
3.6%
studios426
 
2.9%
take-two422
 
2.9%
Other values (346)7372
50.3%
2025-12-03T10:32:25.879622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e11918
 
11.1%
t10116
 
9.4%
n8942
 
8.3%
a7626
 
7.1%
o6859
 
6.4%
i6661
 
6.2%
r6310
 
5.9%
6286
 
5.9%
m3919
 
3.7%
c3035
 
2.8%
Other values (54)35596
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)107268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e11918
 
11.1%
t10116
 
9.4%
n8942
 
8.3%
a7626
 
7.1%
o6859
 
6.4%
i6661
 
6.2%
r6310
 
5.9%
6286
 
5.9%
m3919
 
3.7%
c3035
 
2.8%
Other values (54)35596
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)107268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e11918
 
11.1%
t10116
 
9.4%
n8942
 
8.3%
a7626
 
7.1%
o6859
 
6.4%
i6661
 
6.2%
r6310
 
5.9%
6286
 
5.9%
m3919
 
3.7%
c3035
 
2.8%
Other values (54)35596
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)107268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e11918
 
11.1%
t10116
 
9.4%
n8942
 
8.3%
a7626
 
7.1%
o6859
 
6.4%
i6661
 
6.2%
r6310
 
5.9%
6286
 
5.9%
m3919
 
3.7%
c3035
 
2.8%
Other values (54)35596
33.2%

NA_Sales
Real number (ℝ)

High correlation  Zeros 

Distinct345
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.719943
Minimum0
Maximum4136
Zeros2311
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2025-12-03T10:32:26.022509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q325
95-th percentile125
Maximum4136
Range4136
Interquartile range (IQR)25

Descriptive statistics

Standard deviation104.34994
Coefficient of variation (CV)3.3968141
Kurtosis472.0554
Mean30.719943
Median Absolute Deviation (MAD)8
Skewness17.004451
Sum256788
Variance10888.909
MonotonicityNot monotonic
2025-12-03T10:32:26.130424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02311
27.6%
2280
 
3.3%
4262
 
3.1%
7249
 
3.0%
3249
 
3.0%
6247
 
3.0%
5243
 
2.9%
8235
 
2.8%
1233
 
2.8%
9208
 
2.5%
Other values (335)3842
46.0%
ValueCountFrequency (%)
02311
27.6%
1233
 
2.8%
2280
 
3.3%
3249
 
3.0%
4262
 
3.1%
5243
 
2.9%
6247
 
3.0%
7249
 
3.0%
8235
 
2.8%
9208
 
2.5%
ValueCountFrequency (%)
41361
< 0.1%
29081
< 0.1%
26931
< 0.1%
23201
< 0.1%
15681
< 0.1%
15611
< 0.1%
15001
< 0.1%
14441
< 0.1%
13961
< 0.1%
12781
< 0.1%

EU_Sales
Real number (ℝ)

High correlation  Zeros 

Distinct254
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.067711
Minimum0
Maximum2896
Zeros3002
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2025-12-03T10:32:26.235108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q312
95-th percentile67
Maximum2896
Range2896
Interquartile range (IQR)12

Descriptive statistics

Standard deviation60.936947
Coefficient of variation (CV)3.7925094
Kurtosis689.69995
Mean16.067711
Median Absolute Deviation (MAD)2
Skewness19.507019
Sum134310
Variance3713.3115
MonotonicityNot monotonic
2025-12-03T10:32:26.347603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03002
35.9%
1697
 
8.3%
2617
 
7.4%
3425
 
5.1%
4329
 
3.9%
5275
 
3.3%
6210
 
2.5%
7175
 
2.1%
8157
 
1.9%
9130
 
1.6%
Other values (244)2342
28.0%
ValueCountFrequency (%)
03002
35.9%
1697
 
8.3%
2617
 
7.4%
3425
 
5.1%
4329
 
3.9%
5275
 
3.3%
6210
 
2.5%
7175
 
2.1%
8157
 
1.9%
9130
 
1.6%
ValueCountFrequency (%)
28961
< 0.1%
12761
< 0.1%
10951
< 0.1%
10931
< 0.1%
9191
< 0.1%
9181
< 0.1%
9141
< 0.1%
9091
< 0.1%
8891
< 0.1%
8491
< 0.1%

JP_Sales
Real number (ℝ)

Zeros 

Distinct230
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.308889
Minimum0
Maximum1022
Zeros4807
Zeros (%)57.5%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2025-12-03T10:32:26.462208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile53
Maximum1022
Range1022
Interquartile range (IQR)6

Descriptive statistics

Standard deviation41.215915
Coefficient of variation (CV)3.6445593
Kurtosis117.48137
Mean11.308889
Median Absolute Deviation (MAD)0
Skewness8.9494498
Sum94531
Variance1698.7517
MonotonicityNot monotonic
2025-12-03T10:32:26.825364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04807
57.5%
2398
 
4.8%
1370
 
4.4%
3281
 
3.4%
4216
 
2.6%
5171
 
2.0%
6161
 
1.9%
8128
 
1.5%
7125
 
1.5%
988
 
1.1%
Other values (220)1614
 
19.3%
ValueCountFrequency (%)
04807
57.5%
1370
 
4.4%
2398
 
4.8%
3281
 
3.4%
4216
 
2.6%
5171
 
2.0%
6161
 
1.9%
7125
 
1.5%
8128
 
1.5%
988
 
1.1%
ValueCountFrequency (%)
10221
< 0.1%
7201
< 0.1%
6811
< 0.1%
6501
< 0.1%
6041
< 0.1%
5651
< 0.1%
5381
< 0.1%
5331
< 0.1%
5321
< 0.1%
4871
< 0.1%

Other_Sales
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct122
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2410575
Minimum0
Maximum1057
Zeros3218
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2025-12-03T10:32:26.934907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile21
Maximum1057
Range1057
Interquartile range (IQR)4

Descriptive statistics

Standard deviation22.941531
Coefficient of variation (CV)4.3772714
Kurtosis909.58059
Mean5.2410575
Median Absolute Deviation (MAD)1
Skewness24.747955
Sum43810
Variance526.31386
MonotonicityNot monotonic
2025-12-03T10:32:27.052299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03218
38.5%
11691
20.2%
2812
 
9.7%
3473
 
5.7%
4340
 
4.1%
5251
 
3.0%
6201
 
2.4%
7183
 
2.2%
8127
 
1.5%
990
 
1.1%
Other values (112)973
 
11.6%
ValueCountFrequency (%)
03218
38.5%
11691
20.2%
2812
 
9.7%
3473
 
5.7%
4340
 
4.1%
5251
 
3.0%
6201
 
2.4%
7183
 
2.2%
8127
 
1.5%
990
 
1.1%
ValueCountFrequency (%)
10571
< 0.1%
8441
< 0.1%
7531
< 0.1%
3961
< 0.1%
3291
< 0.1%
2951
< 0.1%
2881
< 0.1%
2841
< 0.1%
2741
< 0.1%
2241
< 0.1%

Global_Sales
Real number (ℝ)

High correlation 

Distinct517
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.371815
Minimum1
Maximum8253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2025-12-03T10:32:27.162681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median18
Q351
95-th percentile238.1
Maximum8253
Range8252
Interquartile range (IQR)45

Descriptive statistics

Standard deviation199.39486
Coefficient of variation (CV)3.1464281
Kurtosis432.22194
Mean63.371815
Median Absolute Deviation (MAD)15
Skewness15.536535
Sum529725
Variance39758.308
MonotonicityNot monotonic
2025-12-03T10:32:27.267984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2548
 
6.6%
3407
 
4.9%
1316
 
3.8%
4314
 
3.8%
5305
 
3.6%
7264
 
3.2%
6262
 
3.1%
9235
 
2.8%
8227
 
2.7%
11199
 
2.4%
Other values (507)5282
63.2%
ValueCountFrequency (%)
1316
3.8%
2548
6.6%
3407
4.9%
4314
3.8%
5305
3.6%
6262
3.1%
7264
3.2%
8227
2.7%
9235
2.8%
10194
 
2.3%
ValueCountFrequency (%)
82531
< 0.1%
40241
< 0.1%
35521
< 0.1%
32771
< 0.1%
31371
< 0.1%
30261
< 0.1%
29801
< 0.1%
28921
< 0.1%
28321
< 0.1%
28311
< 0.1%

Critic_Score
Real number (ℝ)

High correlation  Missing 

Distinct78
Distinct (%)2.0%
Missing4383
Missing (%)52.4%
Infinite0
Infinite (%)0.0%
Mean69.187626
Minimum19
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2025-12-03T10:32:27.371454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile43.75
Q161
median71
Q379
95-th percentile89
Maximum98
Range79
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.756481
Coefficient of variation (CV)0.19882863
Kurtosis0.099179935
Mean69.187626
Median Absolute Deviation (MAD)9
Skewness-0.57126486
Sum275090
Variance189.24076
MonotonicityNot monotonic
2025-12-03T10:32:27.495000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71137
 
1.6%
70128
 
1.5%
69120
 
1.4%
80120
 
1.4%
74118
 
1.4%
72118
 
1.4%
73118
 
1.4%
68116
 
1.4%
75116
 
1.4%
66114
 
1.4%
Other values (68)2771
33.1%
(Missing)4383
52.4%
ValueCountFrequency (%)
191
 
< 0.1%
202
 
< 0.1%
231
 
< 0.1%
242
 
< 0.1%
252
 
< 0.1%
264
< 0.1%
274
< 0.1%
286
0.1%
291
 
< 0.1%
305
0.1%
ValueCountFrequency (%)
982
 
< 0.1%
9710
 
0.1%
9614
 
0.2%
9511
 
0.1%
9418
 
0.2%
9327
0.3%
9227
0.3%
9132
0.4%
9042
0.5%
8951
0.6%

Critic_Count
Real number (ℝ)

High correlation  Missing 

Distinct105
Distinct (%)2.6%
Missing4383
Missing (%)52.4%
Infinite0
Infinite (%)0.0%
Mean28.53999
Minimum4
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2025-12-03T10:32:27.618546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q112
median24
Q340
95-th percentile71
Maximum113
Range109
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.42759
Coefficient of variation (CV)0.71575325
Kurtosis0.73232932
Mean28.53999
Median Absolute Deviation (MAD)13
Skewness1.0687436
Sum113475
Variance417.28645
MonotonicityNot monotonic
2025-12-03T10:32:27.733929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4143
 
1.7%
5135
 
1.6%
17113
 
1.4%
12113
 
1.4%
7108
 
1.3%
6105
 
1.3%
9104
 
1.2%
10102
 
1.2%
8101
 
1.2%
1899
 
1.2%
Other values (95)2853
34.1%
(Missing)4383
52.4%
ValueCountFrequency (%)
4143
1.7%
5135
1.6%
6105
1.3%
7108
1.3%
8101
1.2%
9104
1.2%
10102
1.2%
1197
1.2%
12113
1.4%
1383
1.0%
ValueCountFrequency (%)
1131
< 0.1%
1071
< 0.1%
1061
< 0.1%
1051
< 0.1%
1041
< 0.1%
1031
< 0.1%
1021
< 0.1%
1012
< 0.1%
1002
< 0.1%
991
< 0.1%

User_Score
Text

Missing 

Distinct88
Distinct (%)1.8%
Missing3528
Missing (%)42.2%
Memory size65.4 KiB
2025-12-03T10:32:27.875087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8091492
Min length1

Characters and Unicode

Total characters13571
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.2%

Sample

1st row7.9
2nd row6.6
3rd row7.4
4th row7.8
5th row7.9
ValueCountFrequency (%)
tbd1132
23.4%
8165
 
3.4%
8.2160
 
3.3%
7.8155
 
3.2%
8.3137
 
2.8%
8.1136
 
2.8%
8.5130
 
2.7%
7.9126
 
2.6%
7.5122
 
2.5%
7.7115
 
2.4%
Other values (78)2453
50.8%
2025-12-03T10:32:28.143062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.3238
23.9%
81646
12.1%
71454
10.7%
t1132
 
8.3%
b1132
 
8.3%
d1132
 
8.3%
6941
 
6.9%
5688
 
5.1%
9519
 
3.8%
4468
 
3.4%
Other values (4)1221
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)13571
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.3238
23.9%
81646
12.1%
71454
10.7%
t1132
 
8.3%
b1132
 
8.3%
d1132
 
8.3%
6941
 
6.9%
5688
 
5.1%
9519
 
3.8%
4468
 
3.4%
Other values (4)1221
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13571
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.3238
23.9%
81646
12.1%
71454
10.7%
t1132
 
8.3%
b1132
 
8.3%
d1132
 
8.3%
6941
 
6.9%
5688
 
5.1%
9519
 
3.8%
4468
 
3.4%
Other values (4)1221
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13571
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.3238
23.9%
81646
12.1%
71454
10.7%
t1132
 
8.3%
b1132
 
8.3%
d1132
 
8.3%
6941
 
6.9%
5688
 
5.1%
9519
 
3.8%
4468
 
3.4%
Other values (4)1221
 
9.0%

User_Count
Real number (ℝ)

High correlation  Missing 

Distinct641
Distinct (%)17.3%
Missing4660
Missing (%)55.7%
Infinite0
Infinite (%)0.0%
Mean180.2625
Minimum4
Maximum9851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2025-12-03T10:32:28.282447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q111
median28
Q3100
95-th percentile866.2
Maximum9851
Range9847
Interquartile range (IQR)89

Descriptive statistics

Standard deviation576.98847
Coefficient of variation (CV)3.2008235
Kurtosis94.018586
Mean180.2625
Median Absolute Deviation (MAD)21
Skewness8.2551151
Sum666791
Variance332915.69
MonotonicityNot monotonic
2025-12-03T10:32:28.394378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4159
 
1.9%
5156
 
1.9%
6156
 
1.9%
8134
 
1.6%
7111
 
1.3%
9101
 
1.2%
1093
 
1.1%
1192
 
1.1%
1273
 
0.9%
1373
 
0.9%
Other values (631)2551
30.5%
(Missing)4660
55.7%
ValueCountFrequency (%)
4159
1.9%
5156
1.9%
6156
1.9%
7111
1.3%
8134
1.6%
9101
1.2%
1093
1.1%
1192
1.1%
1273
0.9%
1373
0.9%
ValueCountFrequency (%)
98511
< 0.1%
90731
< 0.1%
86651
< 0.1%
80031
< 0.1%
75121
< 0.1%
73221
< 0.1%
70641
< 0.1%
63831
< 0.1%
61571
< 0.1%
53111
< 0.1%

Developer
Text

Missing 

Distinct1126
Distinct (%)23.1%
Missing3489
Missing (%)41.7%
Memory size65.4 KiB
2025-12-03T10:32:28.606431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length80
Median length44
Mean length13.639014
Min length2

Characters and Unicode

Total characters66422
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique478 ?
Unique (%)9.8%

Sample

1st rowTraveller's Tales
2nd rowTraveller's Tales
3rd rowTraveller's Tales
4th rowTraveller's Tales
5th rowTraveller's Tales
ValueCountFrequency (%)
games589
 
6.1%
studios459
 
4.7%
software315
 
3.2%
entertainment303
 
3.1%
interactive169
 
1.7%
capcom132
 
1.4%
studio125
 
1.3%
inc104
 
1.1%
visual104
 
1.1%
concepts101
 
1.0%
Other values (1176)7296
75.2%
2025-12-03T10:32:28.956069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5904
 
8.9%
a5114
 
7.7%
4827
 
7.3%
t4492
 
6.8%
o4307
 
6.5%
i4243
 
6.4%
n3796
 
5.7%
s3180
 
4.8%
r3156
 
4.8%
m2253
 
3.4%
Other values (63)25150
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)66422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5904
 
8.9%
a5114
 
7.7%
4827
 
7.3%
t4492
 
6.8%
o4307
 
6.5%
i4243
 
6.4%
n3796
 
5.7%
s3180
 
4.8%
r3156
 
4.8%
m2253
 
3.4%
Other values (63)25150
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)66422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5904
 
8.9%
a5114
 
7.7%
4827
 
7.3%
t4492
 
6.8%
o4307
 
6.5%
i4243
 
6.4%
n3796
 
5.7%
s3180
 
4.8%
r3156
 
4.8%
m2253
 
3.4%
Other values (63)25150
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)66422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5904
 
8.9%
a5114
 
7.7%
4827
 
7.3%
t4492
 
6.8%
o4307
 
6.5%
i4243
 
6.4%
n3796
 
5.7%
s3180
 
4.8%
r3156
 
4.8%
m2253
 
3.4%
Other values (63)25150
37.9%

Rating
Categorical

Missing 

Distinct8
Distinct (%)0.2%
Missing3561
Missing (%)42.6%
Memory size65.4 KiB
E
1880 
T
1404 
M
772 
E10+
731 
EC
 
8
Other values (3)
 
3

Length

Max length4
Median length1
Mean length1.4595665
Min length1

Characters and Unicode

Total characters7003
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowE10+
2nd rowE10+
3rd rowE10+
4th rowE10+
5th rowE10+

Common Values

ValueCountFrequency (%)
E1880
22.5%
T1404
 
16.8%
M772
 
9.2%
E10+731
 
8.7%
EC8
 
0.1%
K-A1
 
< 0.1%
AO1
 
< 0.1%
RP1
 
< 0.1%
(Missing)3561
42.6%

Length

2025-12-03T10:32:29.078952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-03T10:32:29.176552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e1880
39.2%
t1404
29.3%
m772
16.1%
e10731
 
15.2%
ec8
 
0.2%
k-a1
 
< 0.1%
ao1
 
< 0.1%
rp1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E2619
37.4%
T1404
20.0%
M772
 
11.0%
1731
 
10.4%
0731
 
10.4%
+731
 
10.4%
C8
 
0.1%
A2
 
< 0.1%
K1
 
< 0.1%
-1
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)7003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E2619
37.4%
T1404
20.0%
M772
 
11.0%
1731
 
10.4%
0731
 
10.4%
+731
 
10.4%
C8
 
0.1%
A2
 
< 0.1%
K1
 
< 0.1%
-1
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E2619
37.4%
T1404
20.0%
M772
 
11.0%
1731
 
10.4%
0731
 
10.4%
+731
 
10.4%
C8
 
0.1%
A2
 
< 0.1%
K1
 
< 0.1%
-1
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E2619
37.4%
T1404
20.0%
M772
 
11.0%
1731
 
10.4%
0731
 
10.4%
+731
 
10.4%
C8
 
0.1%
A2
 
< 0.1%
K1
 
< 0.1%
-1
 
< 0.1%
Other values (3)3
 
< 0.1%

Interactions

2025-12-03T10:32:22.359585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:14.584044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:15.833767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:16.636604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:17.444769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:18.444291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.223626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.996989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:21.302074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:22.446733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:14.719605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:15.958158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:16.740884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:17.539176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:18.542982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.313117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:20.093884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:21.401810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:22.521724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:14.792670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:16.035127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:16.831925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:17.621939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:18.621580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.394183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:20.185441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:21.496584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:22.611270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:14.878641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:16.111393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:16.920759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:17.712923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:18.705631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.473666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:20.279165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:21.844393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:22.697686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:14.974600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:16.194903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:17.005977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:17.802724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:18.785512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.568549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:20.377857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:21.932455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:22.781429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:15.456028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:16.270493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:17.094567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:18.078770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:18.871294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.650111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:20.479848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:22.018938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:22.858417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:15.535813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:16.348749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:17.172093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:18.169329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:18.965336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.721846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:20.578748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:22.096464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:22.949997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:15.618838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:16.438338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:17.272286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:18.265043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.052064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.814111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:20.678637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:22.198233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:23.035310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:15.699812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:16.540192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:17.359079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:18.358356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.137746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:19.910155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:21.149807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-12-03T10:32:22.280554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-12-03T10:32:29.259122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Critic_CountCritic_ScoreEU_SalesGenreGlobal_SalesJP_SalesNA_SalesOther_SalesPlatformRatingUser_CountYear_of_Release
Critic_Count1.0000.4630.4070.0920.4140.3900.3390.4250.1630.1520.6220.185
Critic_Score0.4631.0000.3360.0690.3640.3030.2860.3320.0860.0840.5230.043
EU_Sales0.4070.3361.0000.0200.710-0.0880.7060.7770.0000.0000.444-0.042
Genre0.0920.0690.0201.0000.0210.0530.0280.0000.1810.2750.0400.111
Global_Sales0.4140.3640.7100.0211.0000.2430.8020.8180.0540.0000.400-0.164
JP_Sales0.3900.303-0.0880.0530.2431.000-0.1230.0090.1220.0530.402-0.067
NA_Sales0.3390.2860.7060.0280.802-0.1231.0000.7910.0780.0310.283-0.112
Other_Sales0.4250.3320.7770.0000.8180.0090.7911.0000.0000.0060.4160.056
Platform0.1630.0860.0000.1810.0540.1220.0780.0001.0000.2020.0870.655
Rating0.1520.0840.0000.2750.0000.0530.0310.0060.2021.0000.0880.135
User_Count0.6220.5230.4440.0400.4000.4020.2830.4160.0870.0881.0000.325
Year_of_Release0.1850.043-0.0420.111-0.164-0.067-0.1120.0560.6550.1350.3251.000

Missing values

2025-12-03T10:32:23.179357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-03T10:32:23.497787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-03T10:32:23.718487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NamePlatformYear_of_ReleaseGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_SalesCritic_ScoreCritic_CountUser_ScoreUser_CountDeveloperRating
0LEGO Batman: The VideogameWiiNaNActionWarner Bros. Interactive Entertainment1809702830674.017.07.922.0Traveller's TalesE10+
1LEGO Indiana Jones: The Original AdventuresWiiNaNActionLucasArts1516102123478.022.06.628.0Traveller's TalesE10+
2LEGO Batman: The VideogamePSPNaNActionWarner Bros. Interactive Entertainment564402712873.05.07.410.0Traveller's TalesE10+
3Combat2600NaNActionAtari117701125NaNNaNNaNNaNNaNNaN
4LEGO Harry Potter: Years 5-7WiiNaNActionWarner Bros. Interactive Entertainment694201212476.08.07.813.0Traveller's TalesE10+
5LEGO Harry Potter: Years 5-7X360NaNActionWarner Bros. Interactive Entertainment5137099777.035.07.939.0Traveller's TalesE10+
6Yakuza 4PS3NaNActionSega15136359578.059.08177.0Ryu ga Gotoku StudiosM
7LEGO Harry Potter: Years 5-7PS3NaNActionWarner Bros. Interactive Entertainment36410159176.027.08.348.0Traveller's TalesE10+
8The Lord of the Rings: War in the NorthX360NaNActionWarner Bros. Interactive Entertainment5224088461.048.07.4113.0Snowblind StudiosM
9The Lord of the Rings: War in the NorthPS3NaNActionWarner Bros. Interactive Entertainment25421138263.033.07100.0Snowblind StudiosM
NamePlatformYear_of_ReleaseGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_SalesCritic_ScoreCritic_CountUser_ScoreUser_CountDeveloperRating
8349XCOM 2PS42016.0StrategyTake-Two Interactive48021488.028.08116.0Firaxis GamesT
8350Total War: WARHAMMERPC2016.0StrategySega012011386.077.07.3556.0Creative AssemblyT
8351Culdcept Revolt3DS2016.0StrategyNintendo00606NaNNaNNaNNaNNaNNaN
8352Hearts of Iron IVPC2016.0StrategyParadox Interactive0500583.036.06.9306.0Paradox Development StudioNaN
8353XCOM 2XOne2016.0StrategyTake-Two Interactive2200587.017.08.140.0Firaxis GamesT
8354StellarisPC2016.0StrategyParadox Interactive0400478.057.08569.0Paradox Development StudioNaN
8355Total War Attila: Tyrants & KingsPC2016.0StrategyKoch Media01001NaNNaNNaNNaNNaNNaN
8356Brothers Conflict: Precious BabyPSV2017.0ActionIdea Factory00101NaNNaNNaNNaNNaNNaN
8357Phantasy Star Online 2 Episode 4: Deluxe PackagePS42017.0Role-PlayingSega00404NaNNaNNaNNaNNaNNaN
8358Phantasy Star Online 2 Episode 4: Deluxe PackagePSV2017.0Role-PlayingSega00101NaNNaNNaNNaNNaNNaN